On cherche à étudier l’effet de trois facteurs sur le transcriptome des racines d’Arabidopsis thaliana et de la micro Tomate.
## Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
## EOF within quoted string## Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
## number of items read is not a multiple of the number of columnsCO2
Clustering
load("GenesCO2_At.RData")
# quantification file
data <- read.csv("quantifFiles/QuantifGenes.csv", h = T, sep = ",")
rownames(data) <- data$Gene
genes = which(!(grepl("__", rownames(data))))
not_quant = data[which((grepl("__", rownames(data)))), ]
data = data[genes, grepl("R", colnames(data))]
keep <- rowSums(data) >= 10
data <- data[keep, ]
group <- sapply(colnames(data), getLabel, with.rep = F)
colnames(data) <- sapply(colnames(data), getLabel)
specie = "At"
clusteredGenes <- clustering(sharedBy3, data)****************************************
coseq analysis: Poisson approach & none transformation
K = 2 to 12
Use set.seed() prior to running coseq for reproducible results.
****************************************
Running g = 2 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0"
Running g = 3 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0"
Running g = 4 ...
[1] "Initialization: 1"
[1] "Log-like diff: 9.85664883046411e-11"
Running g = 5 ...
[1] "Initialization: 1"
[1] "Log-like diff: 2.8421709430404e-14"
Running g = 6 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0"
Running g = 7 ...
[1] "Initialization: 1"
[1] "Log-like diff: 5.98119243022666e-08"
Running g = 8 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0"
Running g = 9 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0"
Running g = 10 ...
[1] "Initialization: 1"
[1] "Log-like diff: 8.95090579433599e-09"
Running g = 11 ...
[1] "Initialization: 1"
[1] "Log-like diff: 3.56862983608153e-10"
Running g = 12 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0"
$ICL
$profiles
$boxplots
$probapost_barplots
*************************************************
Model: Poisson
Transformation: none
*************************************************
Clusters fit: 2,3,4,5,6,7,8,9,10,11,12
Clusters with errors: ---
Selected number of clusters via ICL: 12
ICL of selected model: -740101.6
*************************************************
Number of clusters = 12
ICL = -740101.6
*************************************************
Cluster sizes:
Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 Cluster 7
4 2 15 9 6 12 12
Cluster 8 Cluster 9 Cluster 10 Cluster 11 Cluster 12
9 22 13 18 9
Number of observations with MAP > 0.90 (% of total):
131 (100%)
Number of observations with MAP > 0.90 per cluster (% of total per cluster):
Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 Cluster 7
4 2 15 9 6 12 12
(100%) (100%) (100%) (100%) (100%) (100%) (100%)
Cluster 8 Cluster 9 Cluster 10 Cluster 11 Cluster 12
9 22 13 18 9
(100%) (100%) (100%) (100%) (100%) Visualisation en ACP
Class: pca dudi
Call: dudi.pca(df = log(data + 0.1), center = TRUE, scale = TRUE, scannf = FALSE,
nf = 4)
Total inertia: 24
Eigenvalues:
Ax1 Ax2 Ax3 Ax4 Ax5
17.2837 4.7082 0.6320 0.5028 0.2617
Projected inertia (%):
Ax1 Ax2 Ax3 Ax4 Ax5
72.015 19.617 2.633 2.095 1.090
Cumulative projected inertia (%):
Ax1 Ax1:2 Ax1:3 Ax1:4 Ax1:5
72.02 91.63 94.27 96.36 97.45
(Only 5 dimensions (out of 24) are shown)
NULL
Réseau
Nitrate
****************************************
coseq analysis: Poisson approach & none transformation
K = 2 to 12
Use set.seed() prior to running coseq for reproducible results.
****************************************
Running g = 2 ...
[1] "Initialization: 1"
[1] "Log-like diff: 4.59017712728382e-10"
Running g = 3 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0.0273270790755795"
[1] "Log-like diff: 0.00026883443707959"
[1] "Log-like diff: 3.19062152343008e-06"
Running g = 4 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0.00317565071132719"
[1] "Log-like diff: 6.45337852631656e-05"
[1] "Log-like diff: 1.32643063111004e-06"
Running g = 5 ...
[1] "Initialization: 1"
[1] "Log-like diff: 2.50261926737494e-08"
Running g = 6 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0.11469606455513"
[1] "Log-like diff: 0.025426511331073"
[1] "Log-like diff: 0.00543256043100371"
[1] "Log-like diff: 0.00115565145672747"
[1] "Log-like diff: 0.000245569560078707"
Running g = 7 ...
[1] "Initialization: 1"
[1] "Log-like diff: 3.85815361880759e-08"
Running g = 8 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0.0269953964198315"
[1] "Log-like diff: 0.00258195816950035"
[1] "Log-like diff: 0.000242992206885617"
[1] "Log-like diff: 2.48859676972302e-05"
[1] "Log-like diff: 2.3691358670419e-06"
Running g = 9 ...
[1] "Initialization: 1"
[1] "Log-like diff: 1.1980061351835e-06"
Running g = 10 ...
[1] "Initialization: 1"
[1] "Log-like diff: 8.60689688408911e-08"
Running g = 11 ...
[1] "Initialization: 1"
[1] "Log-like diff: 12.0894854576141"
[1] "Log-like diff: 1.04941717138947"
[1] "Log-like diff: 0.652127854852324"
[1] "Log-like diff: 0.302500253305844"
[1] "Log-like diff: 0.144134278076002"
Running g = 12 ...
[1] "Initialization: 1"
[1] "Log-like diff: 3.23944817637312e-08"
$ICL
$profiles
$boxplots
$probapost_barplots
*************************************************
Model: Poisson
Transformation: none
*************************************************
Clusters fit: 2,3,4,5,6,7,8,9,10,11,12
Clusters with errors: ---
Selected number of clusters via ICL: 12
ICL of selected model: -3013888
*************************************************
Number of clusters = 12
ICL = -3013888
*************************************************
Cluster sizes:
Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 Cluster 7
63 23 63 164 53 7 40
Cluster 8 Cluster 9 Cluster 10 Cluster 11 Cluster 12
107 73 121 19 104
Number of observations with MAP > 0.90 (% of total):
837 (100%)
Number of observations with MAP > 0.90 per cluster (% of total per cluster):
Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 Cluster 7
63 23 63 164 53 7 40
(100%) (100%) (100%) (100%) (100%) (100%) (100%)
Cluster 8 Cluster 9 Cluster 10 Cluster 11 Cluster 12
107 73 121 19 104
(100%) (100%) (100%) (100%) (100%) Class: pca dudi
Call: dudi.pca(df = log(data + 0.1), center = TRUE, scale = TRUE, scannf = FALSE,
nf = 4)
Total inertia: 24
Eigenvalues:
Ax1 Ax2 Ax3 Ax4 Ax5
19.1693 3.3531 0.5281 0.3930 0.1205
Projected inertia (%):
Ax1 Ax2 Ax3 Ax4 Ax5
79.872 13.971 2.200 1.638 0.502
Cumulative projected inertia (%):
Ax1 Ax1:2 Ax1:3 Ax1:4 Ax1:5
79.87 93.84 96.04 97.68 98.18
(Only 5 dimensions (out of 24) are shown)
NULL
Iron
****************************************
coseq analysis: Poisson approach & none transformation
K = 2 to 12
Use set.seed() prior to running coseq for reproducible results.
****************************************
Running g = 2 ...
[1] "Initialization: 1"
[1] "Log-like diff: 1.06819442180495e-10"
Running g = 3 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0.0758628365212921"
[1] "Log-like diff: 0.0198623147528725"
[1] "Log-like diff: 0.00525704057190701"
[1] "Log-like diff: 0.00139919104513453"
[1] "Log-like diff: 0.000341532015648127"
Running g = 4 ...
[1] "Initialization: 1"
[1] "Log-like diff: 1.07776350178478"
[1] "Log-like diff: 0.861659587475172"
[1] "Log-like diff: 0.726989034887513"
[1] "Log-like diff: 0.605308819941396"
[1] "Log-like diff: 0.565365021112715"
Running g = 5 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0.0387118669603943"
[1] "Log-like diff: 0.00732243729624926"
[1] "Log-like diff: 0.00136628427148366"
[1] "Log-like diff: 0.000246221735089591"
[1] "Log-like diff: 4.5249045658835e-05"
Running g = 6 ...
[1] "Initialization: 1"
[1] "Log-like diff: 982.457065455328"
[1] "Log-like diff: 1519.26082567653"
[1] "Log-like diff: 1441.62668682535"
[1] "Log-like diff: 646.824080182582"
[1] "Log-like diff: 912.054075951454"
Running g = 7 ...
[1] "Initialization: 1"
[1] "Log-like diff: 3075.61250726608"
[1] "Log-like diff: 4564.13076702637"
[1] "Log-like diff: 3258.49420404661"
[1] "Log-like diff: 427.579259682921"
[1] "Log-like diff: 812.618604827518"
Running g = 8 ...
[1] "Initialization: 1"
[1] "Log-like diff: 880.31287483351"
[1] "Log-like diff: 275.32058591329"
[1] "Log-like diff: 326.646768080913"
[1] "Log-like diff: 72.2925259231334"
[1] "Log-like diff: 302.279208953787"
Running g = 9 ...
[1] "Initialization: 1"
[1] "Log-like diff: 517.306169679352"
[1] "Log-like diff: 389.248458924899"
[1] "Log-like diff: 457.434804311791"
[1] "Log-like diff: 55.4858077387178"
[1] "Log-like diff: 317.507489438035"
Running g = 10 ...
[1] "Initialization: 1"
[1] "Log-like diff: 4903.28126139813"
[1] "Log-like diff: 305.975433627477"
[1] "Log-like diff: 144.421057044739"
[1] "Log-like diff: 318.02902505084"
[1] "Log-like diff: 217.090435952164"
Running g = 11 ...
[1] "Initialization: 1"
[1] "Log-like diff: 541.75667588397"
[1] "Log-like diff: 525.656228746016"
[1] "Log-like diff: 258.144348521114"
[1] "Log-like diff: 161.017591467345"
[1] "Log-like diff: 11.9961394017322"
Running g = 12 ...
[1] "Initialization: 1"
[1] "Log-like diff: 80.4584674238448"
[1] "Log-like diff: 103.550361538123"
[1] "Log-like diff: 156.229544983786"
[1] "Log-like diff: 136.7482838086"
[1] "Log-like diff: 240.690850675943"
$ICL
$profiles
$boxplots
$probapost_barplots
*************************************************
Model: Poisson
Transformation: none
*************************************************
Clusters fit: 2,3,4,5,6,7,8,9,10,11,12
Clusters with errors: ---
Selected number of clusters via ICL: 12
ICL of selected model: -3391000
*************************************************
Number of clusters = 12
ICL = -3391000
*************************************************
Cluster sizes:
Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 Cluster 7
352 22 34 36 238 166 235
Cluster 8 Cluster 9 Cluster 10 Cluster 11 Cluster 12
69 506 747 173 263
Number of observations with MAP > 0.90 (% of total):
2748 (96.73%)
Number of observations with MAP > 0.90 per cluster (% of total per cluster):
Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 Cluster 7
344 22 34 34 222 156 220
(97.73%) (100%) (100%) (94.44%) (93.28%) (93.98%) (93.62%)
Cluster 8 Cluster 9 Cluster 10 Cluster 11 Cluster 12
62 499 735 173 247
(89.86%) (98.62%) (98.39%) (100%) (93.92%) Class: pca dudi
Call: dudi.pca(df = log(data + 0.1), center = TRUE, scale = TRUE, scannf = FALSE,
nf = 4)
Total inertia: 24
Eigenvalues:
Ax1 Ax2 Ax3 Ax4 Ax5
22.03089 1.11520 0.27107 0.11321 0.06945
Projected inertia (%):
Ax1 Ax2 Ax3 Ax4 Ax5
91.7954 4.6467 1.1295 0.4717 0.2894
Cumulative projected inertia (%):
Ax1 Ax1:2 Ax1:3 Ax1:4 Ax1:5
91.80 96.44 97.57 98.04 98.33
(Only 5 dimensions (out of 24) are shown)
NULL
